Imagine a time in the not-too-distant past, when our consumption of media was entirely dictated for us – when we were forced to stay-in, so as not to miss our favourite show, or sit through hours of misguided programming waiting for that long-awaited movie to be broadcast.

Today, this kind of scenario seems almost incomprehensible, thanks to the rapid emergence of new technology that has totally shifted the way we engage with entertainment. Now, we consume media when we want, where we want and how we want, essentially creating our own programming schedules in line with increasing time poverty.

The downside of this of course is the extreme abundance of choice on offer – with approximately 120,000 movies alone to choose from, picking that perfect Friday night film has become a much more challenging undertaking. And so technology has once again had to adapt to fend off indecision.

The rise of machine learning

Artificial intelligence or machine learning is rapidly driving the evolution of a new era of media consumption, one in which choice is once again reduced, but this time in line with viewer demands and preferences. Thanks to complex algorithms made possible by increased computing power, viewers are increasingly able to have their leisure time dictated for them – not by broadcast networks, but by their own behaviour.

While this might sound a bit far-fetched, the concept is an old one, most famously pioneered by the likes of Amazon, who introduced personalised recommendations into their online store. Netflix then upped the ante with their user item collaborative filter, which matched user archetypes to product archetypes to achieve even greater predictive success than the retail giant.

But what both these algorithms lacked was a sense of nuance. People are complex beings, with changing needs, moods and circumstances meaning that our preferences can differ dramatically based on where we are and who we’re with. We might enjoy an action thriller from time to time, but when we’re decompressing on a Friday night with our partner, we’re likely to prefer a lighter form of entertainment. It’s these intricacies of human nature that machines have not been able to crack. Until now that is.

Next-generation algorithms incorporating deep learning are now starting to take shape in the market, offering recommendations based on much more complex data and assumptions. While previous algorithms were quite linear in nature, these evolved versions are able to disassemble a movie or TV show into a series of parts, establishing connections between latent variables to match up viewer and content far more effectively.

What this means is that machines will soon start acting as virtual concierges, taking into account our moods, circumstances and locations to provide curated recommendations that are likely to become increasingly effective as computing power grows ever stronger.

The future of AI in entertainment

So what are the implications of this computerised intervention in our pursuit of entertainment nirvana? Hollywood is sure to welcome such a development with open arms, given AI’s unique ability to pick apart the elements of a film or TV show that are resonating most effectively with various audiences. Essentially, this type of data could in future enable producers to pick and choose from a list of variables that are most likely to guarantee box office results.

While a development like this is likely to frustrate purists and lovers of the cinematic art form, it is ultimately just a more effective form of commercialised film-making. But instead of making movies louder, larger and more laden with bombastic special effects, filmmakers will now be able to pursue commercial gains with increased subtlety.

As machine learning slowly starts to infiltrate various other areas of our lives, leisure time is likely to increase, meaning that filmmakers will need to start working a whole lot harder and producing a lot more content in order to satisfy an ever more idle audience. Could made-to-order movies be on the cards? Given the rate of development in computing, it’s certainly a possibility.

For now, however, users will have to accept having movies made for them by others, but the days of having to make difficult decisions about what to watch are certainly numbered.